@lifeomic/delta

Delta provides tooling for creating and testing AWS Lambda functions that process AWS data streams.

Usage

yarn add @lifeomic/delta

DynamoStreamHandler

This helper provides an abstraction over a DynamoDB Stream Lambda handler.

import { DynamoStreamHandler } from '@lifeomic/delta';

const stream = new DynamoStreamHandler({
  logger,
  // Optionally specify a list of image keys to obfuscate the values of
  loggerObfuscateImageKeys: ['api-secret'],
  parse: (item) => {
    // parse the item using your custom logic, e.g. using zod or ajv.
    return { id: item.id };
  },
  createRunContext: () => {
    /* ... create the "context", e.g. data sources ... */
    return { doSomething: () => null };
  },
  // Optionally specify a concurrency setting for processing events.
  concurrency: 5,
})
  .onInsert(async (ctx, entity) => {
    // INSERT actions receive a single strongly typed new entities
    // (entities are typed based on the `parse` function)
    entity.id;

    // `ctx` contains the nice result of `createRunContext`
    await ctx.doSomething();

    // `ctx` contains a logger by default, which already includes niceties like
    // the AWS request id
    ctx.logger.info('blah blah');
  })
  // The API is chainable to help with readability
  .onModify(async (ctx, oldEntity, newEntity) => {
    // MODIFY actions receive strongly typed old + new entities
    oldEntity.id;
    newEntity.id;
  })
  .onRemove(async (ctx, oldEntity) => {
    // REMOVE actions receive a single strongly typed old entity
    oldEntity.id;

    ctx.logger.info('first remove action');
  })
  // When multiple actions have been added for the same event (e.g. two `onRemove` calls),
  // they are executed in.order.
  .onRemove(async (ctx, oldEntity) => {
    ctx.logger.info('second remove action');
  });

// Provides a dead-simple API for creating the Lambda.
export const handler = stream.lambda();

DynamoStreamHandler also comes with a nice helper for testing: harness(...)

const context = {
  doSomething: jest.fn()
}

const harness = stream.harness({
  /* optionally override the logger */
  logger,
  createRunContext: () => {
    /* optionally override the context, to mock e.g. data sources */
    return context;
  }
})

test('something', async () => {
  // Provides a simple `sendEvent` function
  await harness.sendEvent({
    records: [
      // Simplified, strongly-typed event types for readability
      { type: 'remove', entity: ... },
      { type: 'insert', entity: ... },
      { type: 'modify', oldEntity: ..., newEntity: ... },
    ]
  })

  expect(context.doSomething).toHaveBeenCalled()
})

SQSMessageHandler

This helper provides an abstraction over a SQS message Lambda handler.

import { SQSMessageHandler } from '@lifeomic/delta';

const queue = new SQSMessageHandler({
  logger,
  parseMessage: (message) => {
    /* ... parse from message string -> your custom type ... */
    return JSON.parse(message);
  },
  createRunContext: () => {
    /* ... create the "context", e.g. data sources ... */
    return { doSomething: () => null };
  },
  // Optionally specify a concurrency setting for processing events.
  concurrency: 5,
})
  .onMessage(async (ctx, message) => {
    // `ctx` contains the nice result of `createRunContext`:
    await ctx.doSomething();

    // `ctx` contains a logger by default, which already includes niceties like
    // the AWS request id
    ctx.logger.info('blah blah');
  })
  // Add multiple message handlers for code organization.
  .onMessage(async (ctx, message) => {
    // do something else
  });

// Provides a dead-simple API for creating the Lambda.
export const handler = stream.lambda();

SQSMessageHandler also comes with a nice helper for testing: harness(...)

const context = {
  doSomething: jest.fn()
}

const harness = queue.harness({
  stringifyMessage: (message) => {
    /* stringify from your custom type -> string */
    return JSON.stringify(message)
  },
  /* optionally override the logger */
  logger,
  createRunContext: () => {
    /* optionally override the context, to mock e.g. data sources */
    return context;
  }
})

test('something', async () => {
  // Provides a simple `sendEvent` function
  await harness.sendEvent({
    messages: [
      { /* message 1 */}
      { /* message 2 */}
      { /* message 3 */}
    ]
  })

  expect(context.doSomething).toHaveBeenCalledTimes(3)
})

KinesisEventHandler

This helper provides an abstraction over a Kinesis stream Lambda handler.

import { KinesisEventHandler } from '@lifeomic/delta';

const queue = new KinesisEventHandler({
  logger,
  parseEvent: (event) => {
    /* ... parse from event data -> your custom type ... */
    return JSON.parse(event);
  },
  createRunContext: () => {
    /* ... create the "context", e.g. data sources ... */
    return { doSomething: () => null };
  },
  // Optionally specify a concurrency setting for processing events.
  concurrency: 5,
})
  .onEvent(async (ctx, event) => {
    // `ctx` contains the nice result of `createRunContext`:
    await ctx.doSomething();

    // `ctx` contains a logger by default, which already includes niceties like
    // the AWS request id
    ctx.logger.info('blah blah');
  })
  // Add multiple event handlers for code organization.
  .onEvent(async (ctx, event) => {
    // do something else
  });

// Provides a dead-simple API for creating the Lambda.
export const handler = stream.lambda();

KinesisEventHandler also comes with a nice helper for testing: harness(...)

const context = {
  doSomething: jest.fn()
}

const harness = queue.harness({
  stringifyEvent: (event) => {
    /* stringify from your custom type -> string */
    return JSON.stringify(event)
  },
  /* optionally override the logger */
  logger,
  createRunContext: () => {
    /* optionally override the context, to mock e.g. data sources */
    return context;
  }
})

test('something', async () => {
  // Provides a simple `sendEvent` function
  await harness.sendEvent({
    events: [
      { /* event 1 */}
      { /* event 2 */}
      { /* event 3 */}
    ]
  })

  expect(context.doSomething).toHaveBeenCalledTimes(3)
})

Parallel Processing + Ordering

By default, the abstractions in @lifeomic/delta will process events in parallel. To control the parallelization, specify a concurrency value when creating the handler.

These abstractions also ensure that within a batch of events correct ordering of events is maintained according to the ordering semantics of the upstream event source, even when processing in parallel.

In DynamoStreamHandler, events for the same key will always be processed serially -- events from different keys will be processed in parallel.

In SQSMessageHandler, events with the same MessageGroupId will always processed serially -- events with different MessageGroupId values will be processed in parallel.

In KinesisEventHandler, events with the same partitionKey will always processed serially -- events with different partitionKey values will be processed in parallel.

Note: while the ordering semantics above will always be preserved, events that do not need to be ordered will not necessarily be processed in the same order they were received in the batch (even when using a concurrency value of 1).